Mastering commonsense understanding and reasoning is a pivotal skill
essential for conducting engaging conversations. While there have been several
attempts to create datasets that facilitate commonsense inferences in dialogue
contexts, existing datasets tend to lack in-depth details, restate information
already present in the conversation, and often fail to capture the multifaceted
nature of commonsense reasoning. In response to these limitations, we compile a
new synthetic dataset for commonsense reasoning in dialogue contexts using GPT,
ConvoSense, that boasts greater contextual novelty, offers a higher volume of
inferences per example, and substantially enriches the detail conveyed by the
inferences. Our dataset contains over 500,000 inferences across 12,000
dialogues with 10 popular inference types, which empowers the training of
generative commonsense models for dialogue that are superior in producing
plausible inferences with high novelty when compared to models trained on the
previous datasets. To the best of our knowledge, ConvoSense is the first of its
kind to provide such a multitude of novel inferences at such a large scale.

通过使用 GPT 编译了一个新的合成数据集 ConvoSense，在对话语境中进行常识推理，具有更大的上下文新颖性、更多的推理数量和推理所传达的详细信息丰富，使得针对对话的生成式常识模型在产生合理且具有高新颖性推理时优于之前的数据集训练的模型。据我们所知，ConvoSense 是首个以如此大规模提供如此多新颖推理的数据集。